Methods and apparatus to determine the dimensions of a region of interest of a target object from an image using target object landmarks

ABSTRACT

Methods and apparatus to determine the dimensions of a region of interest of a target object and a class of the target object from an image using target object landmarks are disclosed herein. An example method includes identifying a landmark of a target object in an image based on a match between the landmark and a template landmark; classifying a target object based on the identified landmark; projecting dimensions of the template landmark based on a location of the landmark in the image; and determining a region of interest based on the projected dimensions, the region of interest corresponding to text printed on the target object.

CROSS REFERENCE TO RELATED APPLICATIONS

This patent arises from a patent application that claims priority toU.S. Provisional Patent Application Ser. No. 62/320,881, filed on Apr.11, 2016. The entirety of U.S. Provisional Patent Application Ser. No.62/320,881 is incorporated herein by reference.

FIELD OF THE DISCLOSURE

This disclosure relates generally to image processing, moreparticularly, to methods and apparatus to determine the dimensions of aregion of interest of a target object from an image using target objectlandmarks.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates example dimension determiner to determine dimensionsof a region of interest of an example target object.

FIG. 2 is a block diagram of the example dimension determiner of FIG. 1.

FIGS. 3-7 are flowcharts representative of example machine readableinstructions that may be executed to implement the example dimensiondeterminer of FIGS. 1 and/or 2 to determine the dimensions of the regionof interest and/or categories of the example target object of FIG. 1.

FIGS. 8A-8F illustrate an example image associated with an exampleprocess used by the example dimension determiner of FIGS. 1 and/or 2 todetermine the dimensions of the region of interest of the example targetobject of FIG. 1.

FIG. 9 is a block diagram of an example processor platform that may beutilized to execute the example instructions of FIGS. 3-7 to implementthe example dimension determiner of FIG. 1 and/or FIG. 2.

The figures are not to scale. Wherever possible, the same referencenumbers will be used throughout the drawing(s) and accompanying writtendescription to refer to the same or like parts.

DETAILED DESCRIPTION

Manual text extraction involves a human reviewing an image of a document(or a paper copy of the document) and manually typing the textassociated with that document. Manual extraction has a high level ofaccuracy, but can be cost prohibitive and time consuming. Opticalcharacter recognition (OCR) is a technique to automatically detect,identify, and encode image-based text documents using a computer. Whileusing OCR to extract text is typically faster and cheaper than manualextraction, OCR is less accurate and more prone to error. Furthermore,the accuracy of OCR techniques diminishes when the original imageincluding text to be extracted is of poor quality. For example, while anOCR engine may be able to recognize the clearly printed text of amagazine article imaged using a flatbed scanner with a relatively highlevel of accuracy, the same OCR engine may be much less accurate inrecognizing the text on a receipt imaged using a camera in connectionwith, for example, different (e.g., poor) lighting conditions,crumpledness and/or optical distortions of the receipt, the receipt doesnot lay flat, and/or the captured image cuts off the corners of thereceipt. Accurately determining the dimensions of text of a targetobject in an image increases the accuracy of OCR techniques, therebyreducing the cost associated with manual review to correct for errors.Examples disclosed herein provide an improved accuracy determination ofthe dimensions of a region of interest of target object (e.g.,corresponding to region of text in the target object) than conventionaltechniques.

Additionally, examples disclosed herein may determine the categories ofa target object. As used herein, categories are defined as the sourcesof the receipts represented by their headers, footers or bar codes. Theheader and/or footer are usually presented as graphic design that isbeyond any OCR capability. Without loss of generality, the header and/orfooter presented as some text can also be treated as a graphic design.Examples disclosed herein also provide a determination of theircategories.

Conventional techniques to determine text of a target object from animage include transforming sub-regions of an image onto a rectifiedtarget object by identifying the corners of the target object directlyor indirectly through comparing the target object to the background ofthe image. However, such conventional techniques exhibit difficultyand/or are not able to identify corners. When a direct technique isused, the corners are often confused with many corner-like elementsnaturally presented in the image. In some examples, when the corners arenot well formed or imaged, it is impossible to determine the corners ofa target object. When an indirect technique is used (e.g., usingboundary detection), detecting corners may be difficult or impossiblewhen (a) the background of the captured image includes boundary-likeelements, confusing with the true boundaries, (b) the background issubstantially similar to the target object, making the true boundaryless visible or invisible (e.g., the background has text/font similar tothat of the target object, the background has an intensity similar tothat of the paper of a target), (c) the target object is crumpled ormisshaped, making the boundary curved instead of straight (d) shadowsare present in the captured image, generating false boundaries and/or(e) the boundary(s) of the target object are not within the image.Examples disclosed herein determine a region of interest of a targetobject and a class of the target object (e.g., a company correspondingto the target object) based on identifying and locating a landmark inthe target object. The identification of the landmark determines theclass of the landmark while the location of the landmark provides areference for determination of boundaries of the landmark, andeventually the corners of the landmark. Identifying the region ofinterest based on the landmark of the target object is an indirecttechnique. It alleviates the problems that arise by conventionalindirect techniques of identifying the boundaries of the target objectbased on a comparison of the target object to the background because theboundaries are determined with referenced transition points instead ofall transition points when they are available, and are implied when thetransition points are not available or not formed in a straight line.Examples disclosed herein identify the dimensions of a region ofinterest of a target object using landmarks (e.g., headers, footers,barcodes, advertisements, etc.). Landmarks are independent of thebackground and resilient to noise (e.g., due to printing quality, cameraquality, and/or imaging conditions), thereby increasing the accuracyand/or efficiency of text extraction from captured images. In someexamples, the identified landmark is also used to determine its classbased on a class corresponding to a matched template landmark.

Additionally, conventional techniques perform an OCR process todetermine if a target object is one of interest. For example, suchconventional techniques may perform OCR to identify an address and/orphone number of a company of interest. If identified addresses and/orphone numbers do not correspond to a company of interest, the image isdiscarded, wasting memory and processing resources associated with theOCR process. Examples disclosed herein identify a landmark object beforethe memory and/or processor intensive OCR process occurs. Examplesdisclosed herein identify a class associated with the target objectbased on a template landmark that matches the identified landmark. Inthis manner, example disclosed herein can discard images that do notcorrespond to a company of interest based on the class prior to the OCRprocess, thereby saving processor and/or memory resources. Additionally,examples disclosed herein removes the need for a map between a store(s)and addresses and/or phone numbers corresponding to the store(s),thereby reducing cost and memory resources associated with additionalstorage structures.

Examples disclosed herein receive an image and determine if a targetobject within the image includes a landmark. The landmark is a graphicdesign that is printed on the target object. For example, if the targetobject is a receipt, a landmark may be a header corresponding to acompany's name and/or logo, a footer corresponding to a company's nameand/or logo, or on-object landmark corresponding to an on-receiptpromotion, a bar code, a Quick Response (QR) code, a watermark, etc.Examples disclosed herein compare the received and/or otherwise capturedimage to template landmarks stored in one or more storage device(s)and/or database(s) to determine if the received image includes alandmark (e.g., an imaged landmark). As used herein, a template landmarkis a previously stored landmark. The template landmark is also markedbased on its purpose, such as header, footer, bar-code, or promotion. Insome examples, the template landmark corresponds to a class (e.g., ifthe example landmark is a logo, the class is the company associated withthe logo). Thus, when the template landmark is matched to an imagedlandmark, the class of the imaged landmark is identified. The templatelandmark is used as truth data and an imaged landmark is a landmarkpresented in a received image. When a received image includes an imagedlandmark, examples disclosed herein locate the imaged landmark,determine its class, and determine whether the landmark is a header,footer, or on-object landmark based on the template landmark's mark. Thetype of landmark determines how examples disclosed herein proceed indetermining the dimensions of the target object. More specifically, thetemplate landmark predicates where the true transition point may be,excludes some of the false transition points when a boundary is imaged,and provides a default boundary when the boundary is not imaged or isinvisible. The predication makes it possible to determine the region ofinterest independent of the scale and the orientation.

As discussed in further detail below in connection with FIGS. 8A-8F,examples disclosed herein project the template landmark onto the imagedlandmark of the image using an image overlaying process to generate aleft side search area (LSSA) and a right side search area (RSSA) basedon the location of the template landmark. The example LSSA and RSSAcorresponds to a restricted search range in direction and area. Examplesdisclosed herein process the LSSA and the RSSA to identify a left sideedge of the imaged landmark and a right side edge of the imaged landmarkusing detected transition points and a Hough Transform. In someexamples, a first corner of the region of interest is determined basedon an intersection between the left side imaged landmark edge and abottom or top edge of the imaged template landmark and a second cornerof the region of interest is determined based on an intersection betweenthe right side imaged landmark edge and the bottom or top edge of theimaged template landmark.

Examples disclosed herein determine a left side edge of the targetobject by generating multiple candidate edge lines within a left sidesearch area using Hough line detection method, as even a curved line canbe approximated with straight line within a small section such as withinthe left side search area. The left side edge of the target objectcorresponds to the line among the multiple candidate edge lines that hasthe highest normalized transition point count. Examples disclosed hereindetermine a right side edge of the target object by generating multiplecandidate edge lines within a right side search area using Hough linedetection method, as even a curved line can be approximated withstraight line within a small section such as within the right sidesearch area. The right side edge of the target object corresponds to theline among the multiple candidate edge lines that has the highestnormalized transition point count.

Once the right side edge and the left side edge of the imaged landmarkare determined, examples disclosed herein determine a third corner and afourth corner of the region of interest. Examples disclosed hereinapproximate the left side edge with a straight line by selecting a lineamong all lines starting at the first detected corner and ending at animage boundary with an angle between the imaged landmark left sidegreater than—SA and less than SA, where SA is a predetermined constant.Examples disclosed herein select the line corresponding to the largestnumber of the transition points per unit length. The right side edge isdetermined similarly, except that the starting point is the seconddetected corner. Additionally, examples disclosed herein compute alldark-to-bright transition points in an area defined by a line N pixelsaway from the bottom of the detected landmark, the detected left side,the detected right side, and the boundary of the image. Once thedark-to-bright transition points are computed, examples disclosed hereinuse a Hough transform to find a line furthest from the bottom of theimaged landmark, with an orientation O_(e), such that|O_(e)−O_(l)|<Threshold_(o), where O_(l) is the orientation of thebottom of the imaged landmark. Examples disclosed herein determine thethird corner of the region of interest based on an intersection betweenthe right edge and the lowest text line. Additionally, examplesdisclosed herein determine the fourth corner of the region of interestbased on an intersection between the left edge and the lowest text line.When the landmark is something else (e.g., an on-object landmark, afooter, etc.), the examples may be altered to incorporate differentgeometric constraints to determine corners of a target object. With thecorners detected, there are many methods that can be used to rectify theregion defined by the corners into an image as if the imaged werescanned. Examples disclosed herein effectively remove distortions causedby the imaging process, providing a better image to any OCR system,thereby increasing the accuracy and efficiency of extraction (e.g., textextraction) from images.

FIG. 1 illustrates an example imaging device 100 capturing an image ofan example target object 102. The example imaging device 100 includes anexample camera 104 and an example dimension determiner 106. The exampletarget object 102 includes an example landmark 108 and example text 110.

The example imaging device 100 of FIG. 1 is a device capable ofcapturing an image. In the illustrated example, the imaging device 100is a mobile device (e.g., a cell phone, a tablet, a 2in1, etc.).Alternatively, the imaging device 100 may be a scanner, a web cam, acomputer, a laptop, a digital camera, a three-dimensional sensor, and/orany device that includes a camera or sensor. The example imaging device100 includes the example camera 104. In the illustrated example of FIG.1, the example camera 104 captures an image of all or part of theexample target object 102.

The example target object 102 of FIG. 1 is an object that includes text(e.g., the example text 110) that may be recognized by text recognitionsoftware (e.g., OCR). In the illustrated example of FIG. 1, the targetobject 102 is a receipt. Alternatively, the target object 102 may be anyobject that includes text. The example target object 102 includes thelandmark 108 and the text 110. Although the example landmark 108 is aheader, the example landmark 108 may be a footer, an on-target imagelandmark (e.g., barcode, advertisement, etc.), and/or any other type oflandmark. Examples disclosed herein process an image of the exampletarget object 102 and use the example landmark 108 to increase theaccuracy and of text recognition of the example text 110. In someexamples, the image may be processed in a backend office.

Once an image of the target object 102 of FIG. 1 has been captured, theexample camera 104 and/or another device may filter and/or pre-processthe received image. Additionally, the camera 104 and/or other devicetransmits the image to the example dimension determiner 106. Thedimension determiner 106 retrieves and/or otherwise receives thecaptured image and processes the captured image to identify the examplelandmark 108 by comparing the landmark 108 to one or more storedtemplate landmarks. The dimension determiner 106 determines a region ofinterest of the target object 102 by identifying corners (e.g., fourcorners) corresponding to the example text 110 based on the examplelandmark 108, as further described in conjunction with FIG. 2. In someexamples, the example landmark 108 is processed to indicate the class ofthe target object. For example, when the target object 102 is a storereceipt, the class of the target object may be a store name. Once thedimension determiner 106 determines the corners of region of interestthe target object 102 (e.g., corresponding to the example text 110), thedimension determiner 106 transmits the dimension data and/or any otherextracted data to another process for text recognition. Although theexample dimension determiner 106 is included in the illustrated imagingdevice 100 of FIG. 1, the example dimension determiner 106 may belocated at a remote location. If the dimension determiner 106 is locateda remote location, the example imaging device 100 may provide capturedimages to the dimension determiner 106 via a wired or wirelessconnection (e.g., via a network connection).

FIG. 2 is a block diagram of the example dimension determiner 106 ofFIG. 1 disclosed herein, to determine the corners (e.g., four corners)of a region of interest of the target object 102 of FIG. 1. While theexample dimension determiner 106 is described in conjunction with theexample imaging device 100 and the example target object 102 (FIG. 1),the example dimension determiner 106 may be utilized to determinecorners of any target object captured by any imaging device. The exampledimension determiner 106 includes an example data interface(s) 200, anexample landmark detector 202, an example header landmark storage 204 a,and an example footer landmark storage 204 b, an example on-objectlandmark storage 204 c, and an example corner determiner 206.

The example data interface(s) 200 of FIG. 2 receives and/or otherwiseretrieves captured images from the example camera 104 (FIG. 1). In someexamples, the example data interface(s) 200 interfaces with anotherdevice (e.g., a user interface) to communicate with a user when alandmark has not been found in the image, a manual intervention isneeded, and/or any other error occurred. Additionally, the example datainterface(s) 200 may serve as a control center to manage the wholesystem.

The example landmark detector 202 of FIG. 2 identifies and locateslandmarks in the captured image of the example target object 102. Insome examples, the landmark detector 202 uses planar object detection tocompare parts of the captured image to template landmarks stored in thelandmark storage (e.g., the example header landmark storage 204 a, theexample footer landmark storage 204 b, and/or the example on-objectlandmark storage 204 c). Alternatively, any type of image processing maybe used to determine a match between a captured (e.g., imaged) landmarkand a template (e.g., truth) landmark. The identified landmark is usedto indicate the target's class (e.g., based on the class of the matchedtemplate landmark) and the location of the landmark is used to determinea left side search area, a right side search area of the templatelandmark, the top of the landmark, and the bottom of the landmark. Asfurther described below, the example corner determiner 206 uses the LSSAand the RSSA to determine a first and second corner of the target object102. Additionally, the example landmark detector 202 may determine thatthe imaged landmark is partially cut-off (e.g., the image does notinclude a portion of the landmark). In some examples, in response todetermining that the landmark is cut-off, or no landmark is identified,the landmark detector 202 prompts data interface 200 for manualinterventions. Such manual interventions include discarding the image,confirming a weak detection or classification, defining a boundarymanually, and/or harvesting a new landmark template. In some examples,the landmark detector 202 determines a class of an imaged landmark basedon the class associated with a matched template landmark. In suchexamples, when the landmark detector 202 determines that the class ofthe imaged landmark is not of interest, the example landmark detector202 may prevent further processing of the received image, therebyconserving processor and/or memory resources associated with processingthe image and/or OCRing the image. In this manner, examples disclosedherein do not need to generate and/or store mappings between a companyand addresses and/or phone numbers associated with the company.

The example storage devices 204 a-c of FIG. 2 store template landmarkscorresponding to previously scanned or captured landmarks of targetobjects of interest. The example storage devices 204 a-c may be updatedto include additional collected landmarks from target objects at anytime. The stored landmarks may include additional data corresponding tothe target object 102. For example, the additional data may include datacorresponding to class (e.g., the entity that generated the targetobject) of the target object 102, a format (e.g., layout) of the targetobject 102, a type of landmark (e.g., header, footer, on-object, etc.),etc. In this manner, when a template landmark is matched to an imagedlandmark, the example landmark detector 202 can determine a class of animaged landmark based on the class corresponding to the templatelandmark. The example header landmark storage 204 a stores templateheader landmarks, the example footer landmark storage 204 b storestemplate footer landmarks, and the example on-object landmark storage204 c stores on-object landmarks (e.g., barcodes, advertisements,watermarks etc.) In some examples, the header landmark storage 204 a,the footer landmark storage 204 b, and/or the on-object landmark storage204 c may be combined into one storage device (e.g., a single database).In some examples, the storage devices 204 a-c may be located in a remotelocation. In such examples, the landmark detector 202 may communicatewith the storage devices 204 a-c via a wired or wireless connection. Insome examples, when the landmark detector 202 cannot detect a landmark,the landmark detector 202 may instruct the data interface(s) 200 to sendan error message to a user identifying the problem (e.g., via a userinterface).

The example corner determiner 206 of FIG. 2 determines corners (e.g.,four corners) for a region of interest of the example target object 102based on the imaged landmark detected by the example landmark detector202. In some examples, such as when the landmark is an on-objectlandmark, the example corner determiner 206 may determine eight cornersof a region of interest. For example, the corner determiner 206 may (A)determine the first four corners of the a first region of interest fortext above the on-object landmark (e.g., in a manner substantiallysimilar to determining four corners of a footer landmark) and (B)determine an additional four corners of a second region of interest fortext below the on-object landmark (e.g., in a manner sustainably similarto determining four corners of a header landmark), or (C) determine thetop two corners above the on-object landmark and the bottom two cornersbelow the on-object landmark to construct the desired four corners.

The example corner determiner 206 of FIG. 2 generates an LSSA and anRSSA to identify a first (e.g., left side) and second (e.g., right side)edge of the imaged landmark based on vertical transition points. Avertical transition point is a point whose intensity is substantiallydifferent (e.g., based on one or more threshold(s)) from a neighboringpoint (e.g., Abs(C(x,y)−C(x+n, y))>T1 and Abs(C(x+n,y)−C(x+n+m,y))<T2for some point (x,y) where C(x,y) is the intensity (C) at location(x,y), n and m are integers corresponding to neighboring points, and T1and T2 are threshold values). In some examples, if the LSSA or the RSSAis entirely outside the captured image, the corner determiner 206 mayinstruct the data interface(s) 200 to send an error message to a useridentifying the problem (e.g., via a user interface). Because the LSSAand the RSSA are relatively small regions (e.g., restricted range indirection and area) compared to the total captured image, the accuracyof identifying the edge as a line increases, regardless of whether theedges of the target object 102 are not substantially straight. Theexample corner determiner 206 determines the first and second cornerbased on an intersection of a bottom or top edge of the located (e.g.,matched) template landmark and the left side and the right side detectedin LSSA, and RSSA, respectively, as the top and bottom edge detection ismuch more reliable than that of the left and the right. For example, ifthe imaged landmark is a header, the example corner determiner 206 maydetermine a first corner based on the bottom edge of the locatedtemplate landmark and the computed left side within LSSA. If the imagedlandmark is a footer, the example corner determiner 206 may determine afirst corner based on the top edge of the template landmark and computedleft side within RSSA

Once the example corner determiner 206 of FIG. 2 determines the landmarksides and the first and second corners of the target object 102, theexample corner determiner 206 projects (e.g., extends) the landmarksides toward the top of the image (e.g., for a footer/on-objectlandmark) and/or the bottom of the image (e.g., for a header/on-objectlandmark) to determine the third and fourth corners of the target object102. Because the target object 102 may not be straight, the projectedlandmark edge may not be the best representation of the edge of thetarget object 102. Thus, the example corner determiner 206 generates oneor more alternate target object edge lines within a threshold range ofthe projected landmark edge to identify the best representation of theedge of the target object 102. The example corner determiner 206determines the optimal edge (e.g., best estimation of the actual targetobject edge) for the left and right side of the target object 102 basedon the generated target object edge line with the highest normalizedtransition point count.

The example corner determiner 206, for each candidate side line,calculates a number of horizontal dark-to-bright transition points (HTP)within a neighborhood of the candidate line. An HTP indicates where aprinted character ends. The example corner determiner 206 make a point(x,y) as an HTP point if C(x,y+n)−C(x, y)>T3 and C(x,y+n)−C(x,y+n+m))<T4where C(x,y) is the intensity (C) at location (x,y), n and m areintegers corresponding to neighboring points, and T2 and T3 arethreshold values. A candidate side is a line starting at detectedcorners extending until the image boundary with an orientation deviatingfrom the corresponding side line no more than a predetermined constantS_(A). Among all candidate lines, a line associated with the largestnumber of HTS per unit is selected as a side line best approximating thetrue boundary of the target object 102. When the number of HTS per unitis smaller than some predetermined threshold, the extension of thedetected sides of the located landmark may be used to approximate theboundary. The example corner determiner 206 determines the third andfourth corners of the target object 102 based on an intersection betweenthe object edges and the lowest text line (e.g., for a header) or thehighest text line (e.g., for a footer), as further described inconjunction with FIG. 7. In some examples (e.g., when such sides cannotbe determined satisfactorily), the corner determiner 206 instructs thedata interface(s) 200 to interface with a user for manual confirmationor intervention.

While example manners of implementing the example dimension determiner106 of FIG. 1 are illustrated in conjunction with FIG. 2, elements,processes and/or devices illustrated in conjunction with FIG. 2 may becombined, divided, re-arranged, omitted, eliminated and/or implementedin any other way. Further, the example data interface(s) 200, theexample landmark detector 202, the example header landmark storage 204a, the example footer landmark storage 204 b, the example on-objectlandmark storage 204 c, the example corner determiner 206, and/or, moregenerally, the example dimension determiner 106 of FIG. 2 may beimplemented by hardware, machine readable instructions, software,firmware and/or any combination of hardware, machine readableinstructions, software and/or firmware. Thus, for example, any of theexample data interface(s) 200, the example landmark detector 202, theexample header landmark storage 204 a, the example footer landmarkstorage 204 b, the example on-object landmark storage 204 c, the examplecorner determiner 206, and/or, more generally, the example dimensiondeterminer 106 of FIG. 2 could be implemented by analog and/or digitalcircuit(s), logic circuit(s), programmable processor(s), applicationspecific integrated circuit(s) (ASIC(s)), programmable logic device(s)(PLD(s)) and/or field programmable logic device(s) (FPLD(s)). Whenreading any of the apparatus or system claims of this patent to cover apurely software and/or firmware implementation, at least one of theexample data interface(s) 200, the example landmark detector 202, theexample header landmark storage 204 a, the example footer landmarkstorage 204 b, the example on-object landmark storage 204 c, the examplecorner determiner 206, and/or, more generally, the example dimensiondeterminer 106 of FIG. 2 is/are hereby expressly defined to include atangible computer readable storage device or storage disk such as amemory, a digital versatile disk (DVD), a compact disk (CD), a Blu-raydisk, etc. storing the software and/or firmware. Further still, theexample dimension determiner 106 of FIG. 2 include elements, processesand/or devices in addition to, or instead of, those illustrated inconjunction with FIGS. 3-7, and/or may include more than one of any orall of the illustrated elements, processes and devices.

Flowcharts representative of example machine readable instructions forimplementing the example dimension determiner 106 of FIG. 2 are shown inconjunction with FIGS. 3-7. In the examples, the machine readableinstructions comprise a program for execution by a processor such as theprocessor 912 shown in the example processor platform 900 discussedbelow in connection with FIG. 9. The program may be embodied in machinereadable instructions stored on a tangible computer readable storagemedium such as a CD-ROM, a floppy disk, a hard drive, a digitalversatile disk (DVD), a Blu-ray disk, or a memory associated with theprocessor 912, but the entire program and/or parts thereof couldalternatively be executed by a device other than the processor 912and/or embodied in firmware or dedicated hardware. Further, although theexample program is described with reference to the flowchartsillustrated in conjunction with FIGS. 3-7, many other methods ofimplementing the example dimension determiner 106 of FIG. 2 mayalternatively be used. For example, the order of execution of the blocksmay be changed, and/or some of the blocks described may be changed,eliminated, or combined. Although the flowcharts of FIGS. 3-7 depictexample operations in an illustrated order, these operations are notexhaustive and are not limited to the illustrated order. In addition,various changes and modifications may be made by one skilled in the artwithin the spirit and scope of the disclosure. For example, blocksillustrated in the flowchart(s) may be performed in an alternative orderor may be performed in parallel.

As mentioned above, the example processes of FIGS. 3-7 may beimplemented using coded instructions (e.g., computer and/or machinereadable instructions) stored on a tangible computer readable storagemedium such as a hard disk drive, a flash memory, a read-only memory(ROM), a compact disk (CD), a digital versatile disk (DVD), a cache, arandom-access memory (RAM) and/or any other storage device or storagedisk in which information is stored for any duration (e.g., for extendedtime periods, permanently, for brief instances, for temporarilybuffering, and/or for caching of the information). As used herein, theterm tangible computer readable storage medium is expressly defined toinclude any type of computer readable storage device and/or storage diskand to exclude propagating signals and to exclude transmission media. Asused herein, “tangible computer readable storage medium” and “tangiblemachine readable storage medium” are used interchangeably. Additionallyor alternatively, the example processes of FIGS. 3-7 may be implementedusing coded instructions (e.g., computer and/or machine readableinstructions) stored on a non-transitory computer and/or machinereadable medium such as a hard disk drive, a flash memory, a read-onlymemory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media. As usedherein, when the phrase “at least” is used as the transition term in apreamble of a claim, it is open-ended in the same manner as the term“comprising” is open ended. In addition, the term “including” isopen-ended in the same manner as the term “comprising” is open-ended.

FIG. 3 is an example flowchart 300 representative of example machinereadable instructions that may be executed to implement the exampledimension determiner 106 of FIGS. 1 and 2 to determine corners (e.g.,four corners) of interest corresponding to the corners of the targetobject 102 of FIG. 1. The example flowchart 300 is described inconjunction with an example image 800 as a shown in FIG. 8A. The examplecaptured image of FIG. 8A includes an example target object 802. Theexample target object 802 may correspond to the example target object102 and/or any target object. FIG. 8A includes the example image 800,the example target object 802, and an example imaged landmark 804.Although the example of FIG. 8A illustrates techniques for determinecorners (e.g., four corners) of a region of interest for the targetobject 802 based on the example landmark 804 being a header, similartechniques may be utilized to determine four corner (e.g., or two setsof four corners) based on the example landmark 804 being a footer and/oran on-object landmark.

At block 302, the example interface(s) 200 (FIG. 2) receives and/orotherwise retrieves the example image 800 of the example target object802. As illustrated in the example image 800 of FIG. 8A, the targetobject 802 includes one corner that is outside of (e.g., cut-off from)the image 800. The example target object 802 of FIG. 8A includes theexample imaged landmark 804, which is a header landmark. At block 304,the example landmark detector 202 (FIG. 2) compares the image 800 (e.g.,a part of the example image 800) to one or more stored templatelandmarks in the example header landmark storage 204 a (FIG. 2) todetect the example imaged landmark 804 in the example target object 802.At block 306, the example landmark detector 202 determines if the targetobject 802 includes a landmark corresponding to a template headerlandmark based on the comparison.

If the example landmark detector 202 determines that the target object802 includes a database landmark corresponding to a template headerlandmark (block 306), the process continues to process ‘A,’ which isfurther described in conjunction with FIG. 4. If the example landmarkdetector 202 determines that the target object 802 does not include alandmark corresponding to a template header landmark (block 306), theexample landmark detector 202 compares the image 800 to the storedlandmarks in the example footer landmark storage 204 b (FIG. 2) todetect the example imaged landmark 804 in the example target object 802(block 308). Alternatively, the example landmark detector 202 maydetermine that the target object 802 includes a header landmark that iscut-off. In such examples, the landmark detector 202 may continue tosearch for an additional landmark (e.g., a footer landmark or anon-object landmark) that is not cut-off the example image 800.

At block 310, the example landmark detector 202 determines if the targetobject 802 includes a landmark corresponding to a template footerlandmark based on the comparison. If the example landmark detector 202determines that the target object 802 includes a footer landmark (block310), the example process continues to process ‘B.’ Process ‘B’ is asimilar process to process ‘A,’ but in a different direction. Forexample, process ‘A’ identifies the first two corners (e.g., uppercorners) based on the template header and determines the last twocorners (e.g., lower corners) accordingly. In contrast, Process ‘B’identifies the first two corners (e.g., lower corners) based on thetemplate footer and determines the last two corners (e.g., uppercorners) accordingly, using a substantially similar process as Process‘A.’ If the example landmark detector 202 determines that the targetobject 802 does not includes a footer landmark (block 310), the examplelandmark detector 202 compares the image 800 to the stored landmarks inthe example on-object landmark storage 204 c (FIG. 2) to detect theexample imaged landmark 804 in the example target object 802 (block312).

At block 314, the example landmark detector 202 determines if the targetobject 802 includes a landmark corresponding to an on-object templatelandmark based on the comparison. If the example landmark detector 202determines that the target object 802 includes an on-object landmark,the example process continues to process ‘C.’ Process ‘C’ is a similarprocess to process ‘A’ and ‘B’, operating in both directions (e.g., upand down from the landmark). For example, process ‘C’ may identify twosets of four corners, the first set in a process similar to process ‘A’and the second set in the process similar to process ‘B.’ In thismanner, the target object 802 is processed both above and below theon-object landmark. Alternatively, process ‘C’ may use process ‘A’ toobtain the lower corners of the example target object 802 and may useprocess ‘B’ to obtain the upper corners of the target object 802.

If the landmark detector 202 determines that the target object 802 doesnot include an on-object landmark, the example landmark detector 202flags the example image 800 (block 316). The flag may includetransmitting instructions to the example data interface(s) 200 to sendan error message to a user of the imaging device 100 (FIG. 1). Forexample, the landmark detector 202 may identify an error in the image(e.g., a message identifying that one or more portions of the object ismissing) and request the user to capture an additional image (e.g., arequest to capture an image with an alternate orientation) of the targetobject 802.

FIG. 4 is an example flowchart 400 representative of example machinereadable instructions that may be executed to implement the exampledimension determiner 106 of FIGS. 1 and 2 to determine the corners(e.g., four corners) corresponding to a region of interest of theexample target object 802. The example flowchart 400 is described inconjunction with the example image 800 as a shown in FIGS. 8B-8F. FIG.8B includes the example image 800, the example target object 802, andthe example landmark 804 of FIG. 8A and example matched templatelandmark corners 806 a-d. FIG. 8C includes the example image 800, theexample target object 802, the example landmark 804, and example matchedtemplate landmark corners 806 a-d of FIG. 8B, an example LSSA 808 and anexample RSSA 810. FIG. 8D is an example of the LSSA 808 of FIG. 8C. Theexample LSSA 808 includes the example matched template landmark corners806 a, 806 d of FIG. 8C, example vertical transition points 811, and anexample imaged landmark edge 812. FIG. 8E includes the example theexample image 800, the example target object 802, the example landmark804, and example matched template landmark corners 806 a-d of FIG. 8B,the example imaged landmark edge 812 of FIG. 8C, an example matchedtemplate landmark bottom edge 814, an example first corner 816, anexample bottom image line 818, example edge limit lines 820 a, 820 b, anexample left side edge 822, an example second corner 824, and an exampleright side edge 826. FIG. 8F includes the example image 800, the exampletarget object 802, the example landmark 804, the example matchedtemplate landmark bottom edge 814, the example first corner 816, theexample bottom image line 118, the example left side edge 822, theexample second corner 824, the example right side edge 826 of FIG. 8E,example text lines 828, example error text lines 829, an example thirdcorner 832, and an example fourth corner 834. Although the examples ofFIGS. 8B-F illustrate techniques for determine corners of the targetobject 802 based on the example landmark 804 being a header, similartechniques may be utilized to determine four corner (e.g., or two setsof four corners) based on the example landmark 804 being a footer and/oran on-object landmark.

At block 402, the example landmark detector 202 determines if the imagedlandmark 804 corresponds to a class of interest. As described above, theexample landmark detector 202 may identify a class of the imagedlandmark 804 based on a class corresponding to the matched templatelandmark. If the class is not a class of interest, then the examplelandmark detector 202 determines that there is no need to perform anyfurther identification because the information within the image 800 isnot of interest. If the class of the imaged landmark does not correspondto a class of interest (block 402), the example landmark detector 202prevents utilization of resources (e.g., processor resources and/ormemory resources associated with identifying the corners and/or OCRingthe image 800) by discarding the image 800 and ending the processes(block 404).

At block 406, the example corner determiner 206 (FIG. 2) projects (e.g.,overlays) the example matched template landmark corners 806 a-d onto theexample image 800 based on the identified imaged landmark 804 of FIG.8B. For example, the corner determiner 206 transforms (e.g., matches) acorresponding template landmark and matches the transformed templatelandmark onto the imaged landmark 804 to project the example matchedtemplate landmark corners 806 a-d when the imaged landmark is matched toa landmark template. The landmark corners correspond to the dimensionsof the corresponding matched template landmark transformed to thedimensions of the imaged landmark 804. At block 408, the example cornerdeterminer 206 determines the example LSSA 808 and the example RSSA 810of FIG. 8C based on the matched template landmark. The LSSA 808 iscentered around the left edge of the matched template landmark and theRSSA 810 is centered around the right edge of the matched templatelandmark. In some examples, the height and the width of the LSSA 808and/or RSSA 810 correspond to the length and width of the matchedtemplate landmark (e.g., represented by the matched template landmarkcorners 806 a-d). For example, the height of the LSSA 808 may besubstantially similar to the height of the matched template landmark andthe width of the LSSA 808 may be a fraction (e.g., a quarter) of thewidth of the matched template landmark. Alternatively, any scalar may beused to generate the dimensions of the LSSA 808 and/or RSSA 810.

At block 410, the example corner determiner 206 determines if the LSSA808 overlaps (e.g., at least partially overlaps) the example image 800.If the example LSSA 808 overlaps the example image 800 (e.g., is atleast partially included in the example image 800) (block 410), theexample corner determiner 206 analyzes the example LSSA 808 to determinea first corner (e.g., the example first corner 816 of FIG. 8E) of theregion of interest and the left example imaged landmark edge 812 (FIGS.8D and 8E) of the imaged landmark 804 (block 412), as further describedin conjunction with FIG. 5. If the example LSSA 808 does not overlap theexample image 800 (e.g., is completely outside the example image 800)(block 410), the example corner determiner 206 determines the firstcorner of the target object 802 and a first (e.g., left) imaged landmarkedge of the example imaged landmark 804 based on the matched templatelandmark (block 414). The example corner determiner 206 may determinethe first corner and the first imaged landmark edge of the target object802 based on the example first matched template landmark corner 806 a(FIG. 8C), example fourth matched template landmark corner 806 d (FIG.8C), and/or any other point within the example LSSA 808.

At block 416, the example corner determiner 206 determines if the RSSA810 overlaps (e.g., at least partially overlaps) the example image 800.If the example RSSA 810 overlaps the example image 800 (block 416), theexample corner determiner 206 analyzes the example RSSA 810 to determinea second corner (e.g., the example second corner 824 of FIG. 8E) and theexample right side edge 826 of the imaged landmark 804 (block 418). Ifthe example RSSA 810 does not overlap the example image 800 (block 416),the example corner determiner 206 determines the second corner of thetarget object 802 and a second (e.g., right) imaged landmark edge of theexample imaged landmark 804 based on the matched template landmark(block 420). The example corner determiner 206 may determine the secondcorner and the second (e.g., right) landmark edge of the target object802 based on the example second matched template landmark corner 806 b(FIG. 8C), based on the example third matched template landmark corner806 c (FIG. 8C), and/or based on any other point within the example RSSA810.

At block 422, the example corner determiner 206 determines the exampleleft side edge 822 of the example target object 802 (FIG. 8E) based onthe first (e.g., left) imaged landmark edge, as further described inconjunction with FIG. 6 and described in further detail below. At block424, the example corner determiner 206 determines the example right sideedge 826 of the example target object 802 (FIG. 8E) based on the second(e.g., right) imaged landmark edge. At block 426, the example cornerdeterminer 206 determines the example third corner 832 and the examplefourth corner 834 of the region of interest of the target object 802based on the example left side edge 822 and the example right side edge826, as further described in conjunction with FIG. 7. At block 428, theexample interface(s) 200 transmits the image, the region of interestdata (e.g., including the example corners 816, 824, 832, 834 of FIG.8F), and additional data (e.g., data related to the landmark) to adevice for further processing, including but not limited to OCR.

FIG. 5 is an example flowchart 412 representative of example machinereadable instructions that may be executed to implement the exampledimension determiner 106 of FIGS. 1 and 2 to analyze the example LSSA808 to determine the example left side edge 822 and the first examplecorner 816 of the region of interest of the example target object 802.The example flowchart 412 of FIG. 5 is described in conjunction withFIGS. 8D and 8E.

At block 500 of the illustrated example of FIG. 5, the example cornerdeterminer 206 (FIG. 2) locates all of the example vertical transitionpoints 811 of FIG. 8F in the example LSSA 808. As described above inconjunction with FIG. 2, the example vertical transition points 811 arepoints having an intensity that varies more than a threshold amount froma neighboring point (e.g., Abs(C(x,y)−C(x+n, y))>T1 andAbs(C(x+n,y)−C(x+n+m,y))<T2 for some point (x,y) where C(x,y) is theintensity (C) at location (x,y), n and m are integers corresponding toneighboring points, and T1 and T2 are threshold values). At block 502,the example corner determiner 206 generates lines of best fit for alllines with a threshold number of vertical transition points. Forexample, if the threshold is five points, then the example cornerdeterminer 206 generates all lines include more than five substantiallylinear vertical transition points. In some examples, the example cornerdeterminer 206 determines the lines using a Hough transform. In someexamples, the example corner determiner 206 removes any lines that arenot within a threshold angle range corresponding to an angle of thematched template landmark. For example, if the orientation of a line ismore than a threshold angle (e.g., 30 degrees) different from theorientation of the matched template landmark, the line is discarded.

At block 504 of the illustrated example of FIG. 5, the example cornerdeterminer 206 determines if there is a line in the example LSSA 808that satisfies a threshold number of vertical transition points. Ifthere is not a line that satisfies the threshold number of verticaltransition points, the example corner determiner 206 determines animaged landmark edge based on the example matched template landmarkcorners 806 a, 806 d (block 506). In the illustrated example of FIG. 8D,the line defined with 806 a and 806 d is denoted as the reference line.If there is a line that satisfies the threshold number of verticaltransition points, the example corner determiner 206 determines astrongest line of orientation as the imaged landmark edge. The strongestline of orientation is the line that fits with the most verticaltransition points (block 508) and whose orientation does not deviatefrom that of the reference line more than some predefined thresholdvalue. In the illustrated example of FIG. 8D, the strongest line oforientation is the example imaged landmark edge line 812. At block 510,the example corner determiner 206 determines the example first corner816 (FIG. 8E) based on an intersection between the imaged landmark edgeline 812 and the example matched template landmark bottom edge 814.

FIG. 6 is an example flowchart 422 representative of example machinereadable instructions that may be executed to implement the exampledimension determiner 106 of FIGS. 1 and 2 to determine the example leftside edge 822 of the example target object 802 based on the exampleimaged landmark edge 812. The example flowchart 422 of FIG. 6 isdescribed in conjunction with FIG. 8E.

At block 600, the example corner determiner 206 (FIG. 2) extends theexample imaged landmark edge 812 to the example bottom image line 818.At block 602, the example corner determiner 206 searches lines betweenthe example first corner 816 and points between example edge limit lines820 a, 820 b on the example bottom image line 818. The first exampleedge limit line 820 a is the intersection point between the bottom imageline 818 and the line starting at the example first corner 816 with anorientation S_(a) degrees clockwise away from the of the reference line.The second example edge limit line 820 b is the intersection pointbetween the example bottom image line 818 and the line starting at theexample first corner 816 with an orientation S_(a) degreescounter-clockwise away from that of the reference line. In theillustrated example of FIG. 8E, the threshold range is represented bythe example edge limit lines 820 a, 820 b.

At block 604, the example corner determiner 206 computes a normalizedtransition point count for each of the multiple lines (e.g., representedby the open circles of FIG. 8E). When a transition point lies in theneighborhood of one of the multiple lines, the count associated with theline is increased by one. The total of such points is divided by thelength of the line to obtain the normalized transition point count. Theline with the highest normalized transition point count corresponds tothe best estimation of the left side edge 822. At block 606, the examplecorner determiner 206 determines if the line with the maximum normalizedtransition point count satisfies a normalized transition point countthreshold. If the example corner determiner 206 determines that there isat least one line that satisfies the normalized transition point countthreshold, the example corner determiner 206 determines the example leftside edge 822 of the example target object 802 based on the line withthe maximum normalized transition point count (e.g., the example leftside edge 822) (block 608). If the example corner determiner 206determines that there are not any lines that satisfy the normalizedtransition point count threshold, the example corner determiner 206determines a left imaged edge of the example target 802 to correspond tothe imaged landmark edge 812 (block 610).

FIG. 7 is an example flowchart 426 representative of example machinereadable instructions that may be executed to implement the exampledimension determiner 106 of FIGS. 1 and 2 to determine the example thirdcorner 832 and the example fourth corner 834 of the region of interestof the example target object 802 based on the example left side edge 822and the example right side edge 826. The example flowchart 426 of FIG. 7is described in conjunction with FIG. 8F.

At block 700, the example corner determiner 206 computes all the examplevertical dark-to-bright transition points (e.g., represented by the dotsin FIG. 8F) within a region defined by the example matched templatelandmark bottom edge 814, the example right side edge 826, the exampleleft side edge 822, and the example bottom image line 818. As describedabove in conjunction with FIG. 2, the example corner determiner 206computes the vertical dark-to-bright transition points to determinewhere text ends vertically (e.g., C(x,y+n)−C(x, y)>T3 andC(x,y+n)−C(x,y+n+m))<T4 for some point (x,y) where C(x,y) is theintensity (C) at (x,y), n and m are integers corresponding toneighboring points, and T2 and T3 are threshold values).

At block 702, the example corner determiner 206 identifies all theexample text lines 828, 829 in the region based on the verticaldark-to-bright transition points. In some examples, the example cornerdeterminer 206 uses a Hough transform to identify the example text lines828, 829. At block 704, the example corner determiner 206 eliminates allthe example error text lines 829 outside a threshold orientation of theexample target object 802. As described above, the orientation of thetarget object 802 is determined based on the location of the matchedlandmark. For example, the orientation may be based on the bottom edgeof the matched landmark, the top edge of the matched landmark, or anaverage of the top and bottom edges. The example error text lines 829are lines that do not fall within the threshold orientation of theexample target object 802 (e.g., within a threshold angle of the angleof the matched template landmark).

At block 706, the example corner determiner 206 identifies the examplebottom text line 830 based on the text line (e.g., of the example textlines 828) closest to the bottom of the image 800. At block 708, theexample corner determiner 206 determines the example third corner 832based on an intersection between the example left side edge 822 and theexample bottom text line 830. At block 710, the example cornerdeterminer 206 determines the example fourth corner 834 based on anintersection between the example right side edge 826 and the examplebottom text line 830. As illustrated in example image 800 of FIG. 8F,the example corners 816, 824, 832, 834 represent the region of interestof the example target object 802. In some examples, the region ofinterest corresponds to the corners of the example target object 802. Insome examples, the region of interest corresponds to a subset of theexample target object 802. By the determination of the corners (e.g.,the four example corners 816, 824, 832, 834), which are practicallyimpossible to determine directly, the example target object 802 can beseparated from the background and be rectified onto an image as if theimage were scanned instead of being imaged by a camera, thereby leadingto more accurate OCR extraction.

FIG. 9 is a block diagram of an example processor platform 900 capableof executing the instructions of FIGS. 4A, 4B, and 5 to implement theexample dimension determiner 106 of FIG. 2. The processor platform 900can be, for example, a server, a personal computer, a mobile device(e.g., a cell phone, a smart phone, a tablet such as an iPad™), apersonal digital assistant (PDA), an Internet appliance, or any othertype of computing device.

The processor platform 900 of the illustrated example includes aprocessor 912. The processor 912 of the illustrated example is hardware.For example, the processor 912 can be implemented by integratedcircuits, logic circuits, microprocessors or controllers from anydesired family or manufacturer.

The processor 912 of the illustrated example includes the example memory212 (e.g., a cache). The example processor 912 of FIG. 9 executes theinstructions of FIGS. 4A, 4B, and 5 to implement the example datainterface(s) 200, the example landmark detector 202, the example headerlandmark storage 204 a, the example footer landmark storage 204 b, theexample on-object landmark storage 204 c, and/or the example cornerdeterminer 206 of FIG. 2 to implement the example dimension determiner106 (FIGS. 1 and 2). The processor 912 of the illustrated example is incommunication with a main memory including a volatile memory 914 and anon-volatile memory 916 via a bus 918. The volatile memory 914 may beimplemented by Synchronous Dynamic Random Access Memory (SDRAM), DynamicRandom Access Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM)and/or any other type of random access memory device. The non-volatilememory 916 may be implemented by flash memory and/or any other desiredtype of memory device. Access to the main memory 914, 916 is controlledby a memory controller.

The processor platform 900 of the illustrated example also includes aninterface circuit 920. The interface circuit 920 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 922 are connectedto the interface circuit 920. The input device(s) 922 permit(s) a userto enter data and commands into the processor 912. The input device(s)can be implemented by, for example, a sensor, a microphone, a camera(still or video), a keyboard, a button, a mouse, a touchscreen, atrack-pad, a trackball, isopoint and/or a voice recognition system.

One or more output devices 924 are also connected to the interfacecircuit 920 of the illustrated example. The output devices 924 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a tactileoutput device, and/or speakers). The interface circuit 920 of theillustrated example, thus, typically includes a graphics driver card, agraphics driver chip or a graphics driver processor.

The interface circuit 920 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network926 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 900 of the illustrated example also includes oneor more mass storage devices 928 for storing software and/or data.Examples of such mass storage devices 928 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAIDsystems, and digital versatile disk (DVD) drives.

The coded instructions 932 of FIGS. 4A, 4B, and 5 may be stored in themass storage device 928, in the volatile memory 914, in the non-volatilememory 916, and/or on a removable tangible computer readable storagemedium such as a CD or DVD.

From the foregoing, it will be appreciated that the above disclosedmethods, apparatus, and articles of manufacture may be used determinethe dimensions of a region of interest of a target object and the classof the target object from an image using target object landmarks toincrease the accuracy and durability of text recognition techniques.Prior techniques of processing images for text recognition techniquesinclude determining the corners of the target object. However, suchprior methods have difficulty and/or are not able to identify cornerswhen (a) the background of the image includes boundary-like elements,(b) the background is substantially similar to the target object, (c)the target object is crumpled or misshaped, (d) shadows are present inthe image, (e) prospective distortion is present, and/or (f) thecorner(s) of the target object are not within the image. Examplesdisclosed herein determine a region of interest of a target object andthe class of the target object based on identifying and locating alandmark in the target object and text in the target object. Identifyingand locating the region of interest based on the landmark/text of thetarget object alleviates the problems that arise by conventionaltechniques of directly locating the corners of the target object becausethe corners might not be imaged, well formed, or distinguishable fromthe background. Examples disclosed herein alleviate such problems thatarise by conventional techniques of indirectly locating corners becausethe landmark provides a reference framework that significantlyeliminates the fast edges that often appear in the background.Furthermore, the landmark may indicate a class of the target objectregardless of if the landmark is text or graphical while OCR techniquescannot determine the class of the target object when the landmark isgraphic. The alleviations and the class determination make the disclosedmethod a much more robust way to construct a rectified image and todetermine the class of the target object, thereby proving a more robustwat to extract textual information from the image of the target object.

Although certain example methods, apparatus and articles of manufacturehave been described herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

1. A method comprising: identifying a landmark of a target object in animage based on a match between the landmark and a template landmark;projecting dimensions of the template landmark based on a location ofthe landmark in the image; determining a region of interest based on theprojected dimensions, the region of interest corresponding to textprinted on the target object.
 2. The method of claim 1, furtherincluding comparing the landmark to a plurality of template landmarks.3. The method of claim 1, wherein the template landmark is transformedto correspond to the size and orientation of the landmark.
 4. The methodof claim 3, wherein the projecting of the dimensions of the templatelandmark is based the size and the orientation.
 5. The method of claim1, further including: generating a left side search area, the left sidesearch area being a region including a first edge of the templatelandmark; determining a first landmark edge by analyzing firsttransition points in the left side search area; generating a right sidesearch area, the right side search area being a region including asecond edge of the template landmark; determining a second landmark edgeby analyzing second transition points in the right side search area;determining a first corner of the region of interest based on a firstintersection between a third edge of the template landmark and the firstlandmark edge; and determining a second corner of the region of interestbased on a second intersection between the third edge of the templatelandmark and the second landmark edge.
 6. The method of claim 5, furtherincluding determining a left side edge of the target object and a rightside edge of the target object, the left side edge corresponding to thefirst landmark edge and the right side edge corresponding to the secondlandmark edge.
 7. The method of claim 6, wherein the left side edge andthe right side edge of the target object has a restricted range indirection and area.
 8. The method of claim 6, further includingdetermining text lines within a region, the region corresponding to thethird edge of the template landmark, the left side edge, the right sideedge, and a bottom image line corresponding to a bottom of the image. 9.The method of claim 8, further including: determining a third corner ofthe region of interest based on a third intersection between the leftside edge and a text line closed to the bottom image line; anddetermining a fourth corner of the region of interest based on a fourthintersection between the right side edge and the text line closed to thebottom image line.
 10. The method of claim 9, wherein the first, second,third, and fourth corners of the region of interest improve an accuracyof text recognition techniques.
 11. The method of claim 1, furtherincluding determining a class of the target object based on the matchedtemplate landmark.
 12. An apparatus comprising: a landmark detector toidentify a landmark of a target object in an image based on a matchbetween the landmark and a template landmark; and a corner determinerto: project dimensions of the template landmark based on a location ofthe landmark in the image; and determine a region of interest based onthe projected dimensions, the region of interest corresponding to textprinted on the target object.
 13. The apparatus of claim 12, wherein thelandmark detector is to compare the landmark to a plurality of templatelandmarks.
 14. The apparatus of claim 12, wherein the corner determineris to transform the template landmark to match the size and orientationof the landmark.
 15. The apparatus of claim 14, wherein the projectingof the dimensions of the template landmark is based the size and theorientation.
 16. The apparatus of claim 12, wherein the cornerdeterminer is to: generate a left side search area, the left side searcharea being a region including a first edge of the template landmark;determine a first landmark edge by analyzing first transition points inthe left side search area; generate a right side search area, the rightside search area being a region including a second edge of the templatelandmark; determine a second landmark edge by analyzing secondtransition points in the right side search area; determine a firstcorner of the region of interest based on a first intersection between athird edge of the template landmark and the first landmark edge; anddetermine a second corner of the region of interest based on a secondintersection between the third edge of the template landmark and thesecond landmark edge.
 17. The apparatus of claim 16, wherein the cornerdeterminer is to determine a left side edge of the target object and aright side edge of the target object, the left side edge correspondingto the first landmark edge and the right side edge corresponding to thesecond landmark edge.
 18. The apparatus of claim 17, wherein the leftside edge and the right side edge of the target object has a restrictedrange in direction and area.
 19. The apparatus of claim 17, wherein thecorner determiner is to determine text lines within a region, the regioncorresponding to the third edge of the template landmark, the left sideedge, the right side edge, and a bottom image line corresponding to abottom of the image.
 20. (canceled)
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 23. Acomputer readable storage medium comprising instructions which, whenexecuted, cause a machine to at least: identify a landmark of a targetobject in an image based on a match between the landmark and a templatelandmark; project dimensions of the template landmark based on alocation of the landmark in the image; and determine a region ofinterest based on the projected dimensions, the region of interestcorresponding to text printed on the target object.
 24. (canceled) 25.(canceled)
 26. (canceled)
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 29. (canceled)30. (canceled)
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